**4.1 Identifying required data**

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

The IER group from the randomized controlled trial in Harvie's paper [42] took a very low-calorie diet (VLCD) (75% restriction) on two consecutive days and for the remaining 5 days consume food for weight maintenance. The VLCD provided 2700 kJ of energy and 50 g protein per day, four portions of vegetables (~80 g per portion), one portion of fruit, a salty low-calorie drink and a multivitamin and mineral supplement. The duration of the intervention was six

Daily morning fasting is based on the Bath Breakfast Project (BBP) [45]. BBP is a randomized controlled trial comparing the effects of daily breakfast consumption relative to extended fasting on energy balance and human health. In a randomized cross-over design, obese men and women extended their overnight fast by omitting breakfast consumption or ingesting a typical carbohydrate-rich breakfast of (521 ± 94 kcal), before an ad libitum pasta lunch 3 h later. The duration of interven-

Fasting every second day (FESD) was experienced in a paper of Nils Halberg [46]. The duration of the intervention was 14 days of fasting every second day for 20 h, giving seven fasting periods. Each fasting period started at 22:00 and ended at 18:00 the following day. During the fasting periods, the subjects could drink water

Another IER approach is tested in the paper of Michelle Harvie from 2013 [47]. The test in latter paper included two intermittent energy and carbohydrate restriction (IECR) regimens, including one which allowed ad libitum protein and fat (IECR PF). Overweight 115 women were randomized to an overall 25% energy restriction, either as an IECR (2500–2717 kJ/d,40 g carbohydrate/d for 2 d/week) or a 25% daily energy restriction (DER – which is type of CER - approximately 6000 kJ/d for 7 d/week) or an IECR PF for a 3-month weight-loss period and

This study described in this chapter aims to predict whether a specific IF intervention would reduce the insulin resistance of an individual with prediabetes. The approach to answer this question is machine learning. The process of machine learning is composed of 5 major steps: The first is identifying the required data and gathering data from various sources. The next step is preparing and Pre-processing the data to have homogeneity. Then the model must be built by selecting the right Machine Learning classifier. The fourth step is to train and test the data and gain insights from the model results. Finally, we might want to

**3.2 Intermittent energy restriction (IER)**

**3.3 Daily morning fasting (DMF)**

**3.4 Fasting every second day (FESD)**

**4. The steps in machine learning**

improve results by feature selection for example.

and were instructed to maintain habitual activities.

**3.5 Intermittent energy and carbohydrate restriction (IECR)**

1 month of weight maintenance (IECR or IECR PF for 1 d/week).

months.

tion was 4 weeks.

**392**

In order to answer the question of this study, authors of 25 published papers that performed randomized clinical trials investigating the IF effects on T2D parameters were asked for the individual data. I received the individual data from 5 out 25 papers [42, 45–48]. The other authors replied that they could not submit the data due to the confidentiality of the participants.

#### **4.2 Processing the data**

#### *4.2.1 Choosing people*

The selection criteria for this research were: basal fasting glucose above 5 mmol/L (90 mg/dL) or BMI (Body Mass Index) above or equal to 25. Those criteria were established since they indicate possible prediabetes [49]. The IDF's 2019 cutoff for fasting glucose indicating prediabetes is 100 mg/dL; we set the cutoff at 90 mg/dL. Finally, 254 individuals who answered the criteria were selected. **Table 1** contains the average values of the numerical attributes of the data. The average values show decrease in weight, BMI, fasting glucose and fasting insulin however we should remember that those are averages therefore we cannot conclude that all the interventions work all the time for all the people. This would be the query that the machine learning approach will investigate.

## *4.2.2 HOMA-IR equation*

The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) has been proven to be a very sensitive test for indicating prediabetes [8]. Insulin resistance can be estimated from fasting glucose and insulin levels. This is shown in the HOMA-IR equation presented as follows:

$$\text{HOMA} - IR = \text{FastigGlucose} \ast \text{Fating Insulin} \tag{1}$$

Prediabetes people or people with T2D usually have a significant insulin resistance. A high score of HOMA-IR indicates a significant insulin resistance. To learn the difference of HOMA-IR before and after the intervention the HOMA-IR using (Eq. (1)) was calculated twice for each of the 254 individuals. Once HOMA-IR was calculated for the basal values of fasting glucose and insulin and once for the values after the intervention. The difference between them represents the insulin resistance reduction.

#### *4.2.3 Types of intermittent fasting interventions*

This study contains 9 different types of interventions, starting from continuous energy restriction – through intermittent energy restriction for two days a week, or


**Table 1.**

*Average values of attribute in selected data.*

daily morning fasting or fasting every second day. Part of the interventions contained specific diets. The names of the different types of the interventions and their description are found in **Table 2** below.

**Table 2** summarizes the different IF regimens included in this study. The reference to each regimen is also shown in **Table 2** for further details.
